Does fiscal decentralization really matter for public service satisfaction?

ABSTRACTThis study explores the impact of Chinese fiscal decentralization on public service satisfaction using the China Family Panel Studies (CFPS) data from 2012 to 2020. The findings reveal that fiscal decentralization has a significantly positive effect on public service satisfaction in China. This result remains robust when subjected to various tests. Moreover, this study finds that the enhancement of public service satisfaction is more pronounced in the eastern regions, compared to the central and western regions. Additionally, this study suggests that both economic pressure and corruption have a negative moderating effect on the promotion of fiscal decentralization on public service satisfaction, while marketization has a positive moderating effect. These findings provide a deeper understanding of the factors that promote public service satisfaction in China.KEYWORDS: fiscal decentralizationpublic service satisfactioneconomic pressuremarketization levelcorruptionJEL CLASSIFICATION: H40 Disclosure statementThe authors have no conflicts of interest to declare that are relevant to the content of this article.Notes1 See China's 14th Five-Year Plan and 2035 Vision issued by Central Committee of the Communist Party of China (CPC), and the URL is http://www.xinhuanet.com/politics/zywj/2020–11/03/c_1126693293.htm.2 See The Notice on Improving Assessment of Political Achievements of Leading Group and Leaders in Local Party and Government issued by the Organization Department of the Central Committee of the Communist Party of China (CPC), and its URL is http://renshi.people.com.cn/n/2013/1210/c139617–23793409.html.3 Please refer to National Bureau of Statistics (NBS) of China and the URL is http://www.stats.gov.cn/.4 In CFPS, the individuals have scored the severity of these seven public services' problems from 0 to 10. In order to address the research needs, this study constructs the indicators by taking these corresponding scores from 10.5 Entropy method, an objective weight calculation method, is widely used in getting a summary of multi-indicator values. Entropy method includes the following steps: (1) Normalize the indicator values. Suppose there are m individuals and n indicators in 2020, and xij (i = 1,2,∙∙∙,m; j = 1,2,∙∙∙,n) are the values of these indicators where i and j represent individual and indicator, respectively. Then the normalized indicator values yij are equal to (xij- xminj)/(xmaxj- xminj) where xmaxj and xminj represent the maximum and minimum values of indicator j. (2) Calculate the entropy of the indicators. Indicator j's entropy ej is equal to –(f1jlnf1j+ f2jlnf2j+∙∙∙+ fmjlnfmj)/lnm where fij = yij/(y1j+ y2j+∙∙∙+ ymj) and let fijlnfij be 0 when fij = 0. (3) Calculate the weights of the indicators. Indicator j's weight wj is equal to gj/(g1+ g2+∙∙∙+ gn) where gj = 1-ej. (4) Calculate the weighted average values. Individual i's weighted average value vi is equal to w1xi1+ w2xi2∙∙∙+ wnxin. By the way, vi is the value of SatSer in 2020. (5) Similarly, this study repeats the above steps for other years and finally get all values of SatSer.6 The URL for NERI is http://www.neri.org.cn. NERI has not yet released data for 2020, so this study uses the estimated data for 2020 obtained based on the average growth rate since 2009.7 The data on number of abuse-of-power criminal cases comes from Procuratorial Yearbook of China. The data on number of public servants comes from China Statistics Yearbook.Additional informationFundingThis work was supported by the National Natural Science Foundation of China (Grant number: 72004150).